2022
DOI: 10.1002/essoar.10512545.1
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Modeling wildfire activity in the western United States with machine learning

Abstract: The annual area burned due to wildfires in the western United States (WUS) increased by more than 300% between 1984 and 2020. However, accounting for the nonlinear, spatially heterogeneous interactions between climate, vegetation, and human predictors driving the trends in fire frequency and sizes at different spatial scales remains a challenging problem for statistical fire models. Here we introduce a novel stochastic machine learning (ML) framework to model observed fire frequencies and sizes in 12 km x 12 k… Show more

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